# Knowledge-based XAI through CBR: There is more to explanations than models can tell

arXiv: https://arxiv.org/abs/2108.10363 · HTML: https://mlsanity.com/paper/2108.10363/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 28 of 28)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [The (Un)reliability of saliency methods](https://mlsanity.com/paper/1711.00867/) (2017)
- [Real Time Image Saliency for Black Box Classifiers](https://mlsanity.com/paper/1705.07857/) (2017)
- [Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions](https://mlsanity.com/paper/1710.04806/) (2017)
- [Representer Point Selection for Explaining Deep Neural Networks](https://mlsanity.com/paper/1811.09720/) (2018)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Interpretations are useful: penalizing explanations to align neural networks with prior knowledge](https://mlsanity.com/paper/1909.13584/) (2019)
- [Making deep neural networks right for the right scientific reasons by interacting with their explanations](https://mlsanity.com/paper/2001.05371/) (2020)
- [Explainable Artificial Intelligence: a Systematic Review](https://mlsanity.com/paper/2006.00093/) (2020)
- [The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification](https://mlsanity.com/paper/1503.01161/) (2015)
- [Interpreting Black Box Predictions using Fisher Kernels](https://mlsanity.com/paper/1810.10118/) (2018)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [Content-Based Citation Recommendation](https://mlsanity.com/paper/1802.08301/) (2018)
- [Learning Finite State Representations of Recurrent Policy Networks](https://mlsanity.com/paper/1811.12530/) (2018)
- [CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humans](https://mlsanity.com/paper/1811.07270/) (2018)
- [Explanation Ontology: A Model of Explanations for User-Centered AI](https://mlsanity.com/paper/2010.01479/) (2020)
- [Data Representing Ground-Truth Explanations to Evaluate XAI Methods](https://mlsanity.com/paper/2011.09892/) (2020)
- [Interpreting Deep Models through the Lens of Data](https://mlsanity.com/paper/2005.03442/) (2020)
- [Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching](https://mlsanity.com/paper/2106.04684/) (2021)

## Built on by (most cited first, 1 of 1)

- [Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes](https://mlsanity.com/paper/2302.05752/) (2023)

## Abstract (arXiv)

The underlying hypothesis of knowledge-based explainable artificial intelligence is the data required for data-centric artificial intelligence agents (e.g., neural networks) are less diverse in contents than the data required to explain the decisions of such agents to humans. The idea is that a classifier can attain high accuracy using data that express a phenomenon from one perspective whereas the audience of explanations can entail multiple stakeholders and span diverse perspectives. We hence propose to use domain knowledge to complement the data used by agents. We formulate knowledge-based explainable artificial intelligence as a supervised data classification problem aligned with the CBR methodology. In this formulation, the inputs are case problems composed of both the inputs and outputs of the data-centric agent and case solutions, the outputs, are explanation categories obtained from domain knowledge and subject matter experts. This formulation does not typically lead to an accurate classification, preventing the selection of the correct explanation category. Knowledge-based explainable artificial intelligence extends the data in this formulation by adding features aligned with domain knowledge that can increase accuracy when selecting explanation categories.